Recently, a camera, called a multi-spectral camera or hyper-spectral camera, has been developed as an image input device for inputting multi-dimensional images. For example, shown in FIG. 1 is a color reproducing apparatus described in a publication of JP-A-11-85952. A subject 101 is taken a multi-spectral image by a field-sequential multi-spectral camera 102 which takes an image onto an imaging device, e.g. CCD 105, while rotating a turret 104 structured with a plurality of band-pass filters by a motor 103. The multi-spectral image a taken by the multi-spectral camera 102 is converted, in a color space converting section 107, into a color-space image b (e.g. XYZ image) not dependent upon the device while making a reference to an input profile 106 of the multi-spectral camera 102. The converted color-space image b, in an output value converting section 109, is further converted into a device value suited for the characteristic of the image output device 110 by making reference to an output profile 108. Thus, an obtained output image c (e.g. multi-primary-color image) is outputted.
Herein, explained is the color-space image b not dependent upon the device. The devices for processing color images include various ones, e.g. scanners, digital cameras, printers and displays. There is one scheme for exchanging image data between these apparatuses that once converts the color image data inputted at an input device into independent color-space image data not dependent upon the device and then converts it into color image data for output to an output device. By thus establishing a conversion of between a signal of the image input device and a color space not dependent upon the device, it is possible to deliver data to an image output device of any kind. Thus, there is no need to determine color conversion processes in the number of combinations of input and output devices.
Meanwhile, in case the color image data inputted at the image input device is converted into independent color-space image data not dependent upon not only the device but also the lighting, the output device can output an image based on the lighting different from the lighting of upon inputting the image.
The independent color space not dependent upon the device, generally, uses XYZ three stimulus values defined by the international standard organization CIE or a color appearance model with L*a*b* color space, L*u*v* color space or CAM97s color space. The color appearance model has an attribute value calculated from XYZ three stimulus values. Consequently, provided that XYZ three stimulus values can be estimated from a signal of an image input/output device, the above color conversion is possible.
Meanwhile, the color space not dependent upon a device and lighting generally uses a spectral reflectance of an object. By integrating a multiplication of a desired lighting on a spectral reflectance, XYZ three stimulus values can be calculated.
In a color reproducing apparatus of FIG. 1, where image input and output devices are remotely located, transmission image has the following cases. Namely, a multi-spectral image a as an output of a multi-spectral camera is transmitted to carry out the subsequent process at the remote site; a color-space image b as an output of the color space converting section 107 is transmitted to carry out the subsequent process at the remote site; or an output image c as an output of the output value converting section 109 is transmitted only for display at the remote site.
In any of the cases, however, the amount of data is huge as compared to the conventional RGB image. For example, where 31-dimensional spectral reflectance information sampled at an interval of 10 nm on a visible range of 400 nm to 700 nm is made into a 512×480 image, 7 MB or greater is required. Consequently, it is preferred to carry out compression to a possible extent even at the present the storage capacity and transmission speed has increased.
There is a conventional method for compressing a multi-spectral image, e.g. “Consideration on a Multi-band Image Compression Method Using a Principal Component Analysis” in a document Display and Imaging 2000, Vol. 8, pp. 301–307. This document discloses four compression methods, i.e. (1) a method of conducting a principal component analysis on a multi-spectral image to delete a higher-order term, (2) a method of dividing a multi-spectral image into rectangular blocks to carry out discrete cosine transformation on a block-by-block basis, (3) a method of conducting a principal component analysis on a multi-spectral image and thereafter dividing it into rectangular blocks to carry out discrete cosine transformation on a block-by-block basis, and (4) a method of dividing a multi-spectral image into rectangular blocks to thereafter carry out principal component analysis and discrete cosine transformation on a block-by-block basis.
These methods carry out principal component analysis on the entire of a multi-spectral image or on each of the rectangularly divided blocks thereof. In case the block size is small, there is a high possibility that the block at its inside be constituted by a similar color and hence a high post-decompression reproduction accuracy. However, there is an increased amount of calculation because of performing principal component analyses commensurate with the number of blocks. Meanwhile, basis functions must be transmitted in an amount corresponding to the number of blocks, resulting in decreased compression ratio. Conversely, in case block size is increased, there is less possibility that the block at its inside be constituted by a similar color. This results in a decrease of post-decompression reproduction accuracy.